






import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
							import torch.distributed.checkpoint as dist_cp
							from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
							from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
							from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
							from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase								=		get_logger(__name__)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=0				)     ->    List[Any]:
							os.makedirs(SCREAMING_SNAKE_CASE     ,     exist_ok=SCREAMING_SNAKE_CASE				)
							with FSDP.state_dict_type(
							    SCREAMING_SNAKE_CASE     ,     fsdp_plugin.state_dict_type     ,     fsdp_plugin.state_dict_config     ,     fsdp_plugin.optim_state_dict_config				):
														_lowercase							:   str 							=   model.state_dict()
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					_lowercase							:   Any 							=   F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
																					_lowercase							:   Dict 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					if accelerator.process_index == 0:
																												logger.info(F"""Saving model to {output_model_file}"""				)
																												torch.save(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												logger.info(F"""Model saved to {output_model_file}"""				)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					_lowercase							:   List[Any] 							=   (
																					    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
																					    if model_index == 0
																					    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
																					)
																					_lowercase							:   List[str] 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Saving model to {output_model_file}"""				)
																					torch.save(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Model saved to {output_model_file}"""				)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					_lowercase							:   Optional[int] 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{MODEL_NAME}_{model_index}"""				)
																					os.makedirs(SCREAMING_SNAKE_CASE     ,     exist_ok=SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Saving model to {ckpt_dir}"""				)
																					_lowercase							:   Union[str, Any] 							=   {'model': state_dict}

																					dist_cp.save_state_dict(
																					    state_dict=SCREAMING_SNAKE_CASE     ,     storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE				)     ,     planner=DefaultSavePlanner()     ,     )
																					logger.info(F"""Model saved to {ckpt_dir}"""				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=0				)     ->    Optional[int]:
							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    SCREAMING_SNAKE_CASE     ,     fsdp_plugin.state_dict_type     ,     fsdp_plugin.state_dict_config     ,     fsdp_plugin.optim_state_dict_config				):
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if type(SCREAMING_SNAKE_CASE				) != FSDP and accelerator.process_index != 0:
																												if not fsdp_plugin.sync_module_states:
																																			raise ValueError(
																																			    'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
																																			    'initializing FSDP object'				)
																												return
																					_lowercase							:   Optional[Any] 							=   F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
																					_lowercase							:   str 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Loading model from {input_model_file}"""				)
																					_lowercase							:   List[Any] 							=   torch.load(SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Model loaded from {input_model_file}"""				)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					_lowercase							:   Optional[int] 							=   (
																					    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
																					    if model_index == 0
																					    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
																					)
																					_lowercase							:   Tuple 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Loading model from {input_model_file}"""				)
																					_lowercase							:   int 							=   torch.load(SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Model loaded from {input_model_file}"""				)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					_lowercase							:   List[str] 							=   (
																					    os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{MODEL_NAME}_{model_index}"""				)
																					    if F"""{MODEL_NAME}""" not in input_dir
																					    else input_dir
																					)
																					logger.info(F"""Loading model from {ckpt_dir}"""				)
																					_lowercase							:   Optional[int] 							=   {'model': model.state_dict()}
																					dist_cp.load_state_dict(
																					    state_dict=SCREAMING_SNAKE_CASE     ,     storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE				)     ,     planner=DefaultLoadPlanner()     ,     )
																					_lowercase							:   List[str] 							=   state_dict['model']
																					logger.info(F"""Model loaded from {ckpt_dir}"""				)
														model.load_state_dict(SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=0				)     ->    Dict:
							os.makedirs(SCREAMING_SNAKE_CASE     ,     exist_ok=SCREAMING_SNAKE_CASE				)
							with FSDP.state_dict_type(
							    SCREAMING_SNAKE_CASE     ,     fsdp_plugin.state_dict_type     ,     fsdp_plugin.state_dict_config     ,     fsdp_plugin.optim_state_dict_config				):
														_lowercase							:   Dict 							=   FSDP.optim_state_dict(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if accelerator.process_index == 0:
																												_lowercase							:   Any 							=   (
																												    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																												)
																												_lowercase							:   Any 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""				)
																												torch.save(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												logger.info(F"""Optimizer state saved in {output_optimizer_file}"""				)
														else:
																					_lowercase							:   Tuple 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{OPTIMIZER_NAME}_{optimizer_index}"""				)
																					os.makedirs(SCREAMING_SNAKE_CASE     ,     exist_ok=SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Saving Optimizer state to {ckpt_dir}"""				)
																					dist_cp.save_state_dict(
																					    state_dict={'optimizer': optim_state}     ,     storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE				)     ,     planner=DefaultSavePlanner()     ,     )
																					logger.info(F"""Optimizer state saved in {ckpt_dir}"""				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=0				)     ->    int:
							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    SCREAMING_SNAKE_CASE     ,     fsdp_plugin.state_dict_type     ,     fsdp_plugin.state_dict_config     ,     fsdp_plugin.optim_state_dict_config				):
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					_lowercase							:   Optional[int] 							=   None
																					# below check should work but currently it isn't working (mostly opytorch issue),
																					# in the meantime disabling it at the cost of excess memory usage
																					# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
																					_lowercase							:   int 							=   (
																					    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																					)
																					_lowercase							:   List[Any] 							=   os.path.join(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""				)
																					_lowercase							:   Optional[Any] 							=   torch.load(SCREAMING_SNAKE_CASE				)
																					logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""				)
														else:
																					_lowercase							:   Dict 							=   (
																					    os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{OPTIMIZER_NAME}_{optimizer_index}"""				)
																					    if F"""{OPTIMIZER_NAME}""" not in input_dir
																					    else input_dir
																					)
																					logger.info(F"""Loading Optimizer from {ckpt_dir}"""				)
																					_lowercase							:   Union[str, Any] 							=   load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()     ,     optimizer_key='optimizer'     ,     storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE				)     ,     )
																					_lowercase							:   Union[str, Any] 							=   optim_state['optimizer']
																					logger.info(F"""Optimizer loaded from {ckpt_dir}"""				)
														_lowercase							:   int 							=   FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														optimizer.load_state_dict(SCREAMING_SNAKE_CASE				)










'''simple docstring'''







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     0
   while num > 0:
      digit_sum += num % 10
      num //= 10
   return digit_sum







def 						_A      ( UpperCAmelCase = 100						):






   '''simple docstring'''







   A__       =     1
   A__       =     2

   for i in range(2      ,max_n + 1						):
      A__       =     pre_numerator
      A__       =     2 * i // 3 if i % 3 == 0 else 1
      A__       =     cur_numerator
      A__       =     e_cont * pre_numerator + temp

   return sum_digits(UpperCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')


